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2005 Fiscal Year Final Research Report Summary

Improvement of Efficiency for Interactive Document Retrieval using Transductive Inference

Research Project

Project/Area Number 16500094
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeSingle-year Grants
Section一般
Research Field Intelligent informatics
Research InstitutionCentral Research Institute of Electric Power Industry

Principal Investigator

ONODA Takashi  Central Research Institute of Electric Power Industry, System Engineering Laboratory, Senior Research Scientist, システム技術研究所, 上席研究員 (40371661)

Co-Investigator(Kenkyū-buntansha) YAMADA Seiji  National Institute of Informatics, Intelligent Systems Research Division, Professor, 知能システム研究系, 教授 (50220380)
Project Period (FY) 2004 – 2005
Keywordsdocument retrieval / support vector machine / interaction / transductive inference / active learning
Research Abstract

In the past, traditional interactive document retrieval system retrieved the remained documents by using only the documents that the user evaluated, and had not used information in the remained documents positively. However, the methods using these documents, which are not evaluated by a user, in recent years is proposed in the machine learning research field. The retrieval efficiency can be expected to be improved rapidly by using these methods based on the not evaluated documents. In addition, the interactive document retrieval system should be designed with considering the recognition load of human being In this research, the research purpose is to research and develop the interactive document retrieval method based on the transductive inference using the artificial intelligence methods, especially the machine learning methods.
In such a purpose, the concept of the transductive inference was introduced into the frame of active learning with the support vector machine in this research. We proposed a novel document selection method which can display the documents, which are near the user's desire and the system's desire. And we developed this method on a computer and evaluated this method using large bench mark datasets.
By the achievement of this research, the user input the keywords to retrieve documents onetime. After this input, the user evaluates the displayed documents and the user can see the relevant documents. Therefore, the user escapes from making keywords to retrieve the documents.

  • Research Products

    (18 results)

All 2005 2004 Other

All Journal Article (18 results)

  • [Journal Article] Non-relevance Feedback for Document Retrieval2005

    • Author(s)
      H.Murata
    • Journal Title

      Proceedings of Artificial Intelligence and Applications 2005

      Pages: 84-89

    • Description
      「研究成果報告書概要(和文)」より
  • [Journal Article] Relevance Feedback Document Retrieval using Support Vector Machines2005

    • Author(s)
      T.Onoda
    • Journal Title

      AM-2003 Post4*roceedings(Springer Lecture Note)

      Pages: 59-73

    • Description
      「研究成果報告書概要(和文)」より
  • [Journal Article] One Class Support Vector Machine based Non^elevance Feedback Document Retrieval2005

    • Author(s)
      T.Onoda
    • Journal Title

      Proceedings of International Joint Conference on Neural Networks 2005

      Pages: 552-557

    • Description
      「研究成果報告書概要(和文)」より
  • [Journal Article] A One Class Classification Approach to Non-Relevance Feedback Document Retrieval2005

    • Author(s)
      T.Onoda
    • Journal Title

      Proceedings of International Conference on Natural Computation 2005

      Pages: 1216-1225

    • Description
      「研究成果報告書概要(和文)」より
  • [Journal Article] Comparison of Retrieval Efficiency between One Class SVM based and SVDD based Non-Relevant Feedback Document Retrieval2005

    • Author(s)
      T.Onoda
    • Journal Title

      Proceedings of the International Conference on Computational Intelligence, Robotics and Autonomous Systems 2005

    • Description
      「研究成果報告書概要(和文)」より
  • [Journal Article] Relevance Feedback Document Retrieval using Support Vector Machines2005

    • Author(s)
      T.Onoda, H.Murata, S.Yamada
    • Journal Title

      AM-2003 Post-Proceedings (Springer Lecture Note)

      Pages: 59-73

    • Description
      「研究成果報告書概要(欧文)」より
  • [Journal Article] Relevance Feedback Document Retrieval using Support Vector Machines2004

    • Author(s)
      T.Onoda
    • Journal Title

      Proceedings of International Joint Conference on Neural Networks 2004

    • Description
      「研究成果報告書概要(和文)」より
  • [Journal Article] 適合フィードバックにおける非適合文書からの文書検索2004

    • Author(s)
      村田 博士
    • Journal Title

      2004年人工知能学会全国大会予稿集

    • Description
      「研究成果報告書概要(和文)」より
  • [Journal Article] Non-Relevance Document Retrieval2004

    • Author(s)
      T.Onoda
    • Journal Title

      Proceedings of IEEE Conference on Cybernetic and Intelligent Systems 2004

      Pages: 456-461

    • Description
      「研究成果報告書概要(和文)」より
  • [Journal Article] Relevance Feedback Document Retrieval using Non-Relevant Documents2004

    • Author(s)
      T.Onoda
    • Journal Title

      人工知能学会研究会資料SIG-KBS-A403

      Pages: 7-12

    • Description
      「研究成果報告書概要(和文)」より
  • [Journal Article] Relevance Feedback Document Retrieval using Support Vector Machines

    • Author(s)
      T.Onoda, H.Murata, S.Yamada
    • Journal Title

      Proceedings of International Joint Conference on Neural Networks 2004

    • Description
      「研究成果報告書概要(欧文)」より
  • [Journal Article] Document Retrieval based on Non-Relevant Documents in Relevance Feedback Document Retrieval (in Japanese)

    • Author(s)
      T.Onoda, H.Murata, S.Yamada
    • Journal Title

      Proceedings of Conference on Japanese Society of Artificial Intelligence 2004

    • Description
      「研究成果報告書概要(欧文)」より
  • [Journal Article] Non-Relevance Document Retrieval

    • Author(s)
      T.Onoda, H.Murata, S.Yamada
    • Journal Title

      Proceedings of IEEE Conference on Cybernetic and Intelligent Systems 2004

      Pages: 456-461

    • Description
      「研究成果報告書概要(欧文)」より
  • [Journal Article] Relevance Feedback Document Retrieval using Non-Relevant Documents

    • Author(s)
      T.Onoda, H.Murata, S.Yamada
    • Journal Title

      Proceedings of Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ and IEICE-SIGAI on Active Mining

      Pages: 7-12

    • Description
      「研究成果報告書概要(欧文)」より
  • [Journal Article] Non-relevance Feedback for Document Retrieval

    • Author(s)
      H.Murata, T.Onoda, S.Yamada
    • Journal Title

      Proceedings of Artificial Intelligence and Applications 2005

      Pages: 84-89

    • Description
      「研究成果報告書概要(欧文)」より
  • [Journal Article] One Class Support Vector Machine based Non-Relevance Feedback Document Retrieval

    • Author(s)
      T.Onoda, H.Murata, S.Yamada
    • Journal Title

      Proceedings of International Joint Conference on Neural Networks 2005

      Pages: 552-557

    • Description
      「研究成果報告書概要(欧文)」より
  • [Journal Article] A One Class Classification Approach to Non-Relevance Feedback Document Retrieval

    • Author(s)
      T.Onoda, H.Murata, S.Yamada
    • Journal Title

      Proceedings of International Conference on Natural Computation 2005

      Pages: 1216-1225

    • Description
      「研究成果報告書概要(欧文)」より
  • [Journal Article] Comparison of Retrieval Efficiency between One Class SVM based and SVDD based Non-Relevant Feedback Document Retrieval

    • Author(s)
      T.Onoda, H.Murata, S.amada
    • Journal Title

      Proceedings of The International Conference on Computational Intelligence, Robotics and Autonomous Systems 2005

    • Description
      「研究成果報告書概要(欧文)」より

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Published: 2007-12-13  

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